Zobrazeno 1 - 10
of 33
pro vyhledávání: '"De Gaspari, Fabio"'
The unprecedented availability of training data fueled the rapid development of powerful neural networks in recent years. However, the need for such large amounts of data leads to potential threats such as poisoning attacks: adversarial manipulations
Externí odkaz:
http://arxiv.org/abs/2403.13523
Autor:
Hitaj, Dorjan, Pagnotta, Giulio, De Gaspari, Fabio, Ruko, Sediola, Hitaj, Briland, Mancini, Luigi V., Perez-Cruz, Fernando
Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in publ
Externí odkaz:
http://arxiv.org/abs/2403.03593
Autor:
Miho, Hristofor, Pagnotta, Giulio, Hitaj, Dorjan, De Gaspari, Fabio, Mancini, Luigi V., Koubouris, Georgios, Godino, Gianluca, Hakan, Mehmet, Diez, Concepcion Muñoz
The easy and accurate identification of varieties is fundamental in agriculture, especially in the olive sector, where more than 1200 olive varieties are currently known worldwide. Varietal misidentification leads to many potential problems for all t
Externí odkaz:
http://arxiv.org/abs/2303.00431
Autor:
Pagnotta, Giulio, De Gaspari, Fabio, Hitaj, Dorjan, Andreolini, Mauro, Colajanni, Michele, Mancini, Luigi V.
Publikováno v:
IEEE Transactions on Information Forensics and Security, 2023
Moving Target Defense and Cyber Deception emerged in recent years as two key proactive cyber defense approaches, contrasting with the static nature of the traditional reactive cyber defense. The key insight behind these approaches is to impose an asy
Externí odkaz:
http://arxiv.org/abs/2303.00387
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future. Consequently, significant effort has been devoted to ransomware detection and mitigation. Behavioral-based ransomwar
Externí odkaz:
http://arxiv.org/abs/2301.11050
Autor:
Piskozub, Michal, De Gaspari, Fabio, Barr-Smith, Frederick, Mancini, Luigi V., Martinovic, Ivan
Economic incentives encourage malware authors to constantly develop new, increasingly complex malware to steal sensitive data or blackmail individuals and companies into paying large ransoms. In 2017, the worldwide economic impact of cyberattacks is
Externí odkaz:
http://arxiv.org/abs/2106.00541
Recent advances in generative machine learning models rekindled research interest in the area of password guessing. Data-driven password guessing approaches based on GANs, language models and deep latent variable models have shown impressive generali
Externí odkaz:
http://arxiv.org/abs/2105.06165
Several cybersecurity domains, such as ransomware detection, forensics and data analysis, require methods to reliably identify encrypted data fragments. Typically, current approaches employ statistics derived from byte-level distribution, such as ent
Externí odkaz:
http://arxiv.org/abs/2103.17059
Autor:
Miho, Hristofor, Pagnotta, Giulio, Hitaj, Dorjan, De Gaspari, Fabio, Mancini, Luigi Vincenzo, Koubouris, Georgios, Godino, Gianluca, Hakan, Mehmet, Diez, Concepción Muñoz
Publikováno v:
In Computers and Electronics in Agriculture January 2024 216
Reliable identification of encrypted file fragments is a requirement for several security applications, including ransomware detection, digital forensics, and traffic analysis. A popular approach consists of estimating high entropy as a proxy for ran
Externí odkaz:
http://arxiv.org/abs/2010.07754